


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCamelCase					:       List[str]			    =     argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
UpperCamelCase					:       Any			    =     parser.parse_args()


UpperCamelCase					:       Tuple			    =     """cpu"""
UpperCamelCase					:       int			    =     """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

UpperCamelCase					:       List[Any]			    =     """path-to-your-trained-model"""
UpperCamelCase					:       Dict			    =     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
    UpperCamelCase					:       Tuple			    =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCamelCase					:       Dict			    =     pipe.to(device)

# to channels last
UpperCamelCase					:       Optional[int]			    =     pipe.unet.to(memory_format=torch.channels_last)
UpperCamelCase					:       Union[str, Any]			    =     pipe.vae.to(memory_format=torch.channels_last)
UpperCamelCase					:       str			    =     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
    UpperCamelCase					:       str			    =     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
UpperCamelCase					:       str			    =     torch.randn(2, 4, 64, 64)
UpperCamelCase					:       Union[str, Any]			    =     torch.rand(1) * 999
UpperCamelCase					:       Any			    =     torch.randn(2, 77, 768)
UpperCamelCase					:       Tuple			    =     (sample, timestep, encoder_hidden_status)
try:
    UpperCamelCase					:       Union[str, Any]			    =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
    UpperCamelCase					:       Optional[Any]			    =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase					:       str			    =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase					:       Tuple			    =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    UpperCamelCase					:       Any			    =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
UpperCamelCase					:       Optional[int]			    =     666
UpperCamelCase					:       Any			    =     torch.Generator(device).manual_seed(seed)
UpperCamelCase					:       int			    =     {"""generator""": generator}
if args.steps is not None:
    UpperCamelCase					:       Tuple			    =     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
    UpperCamelCase					:       str			    =     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")









'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


